Maximizing The Real Value of Big Data Through Insights

May 22, 2018 0 comments

Big data is creating unprecedented opportunities for organizations to achieve faster, better insights that strengthen decision-making. However, the traditional methods and tools for the analysis of data is not sufficient to capture the potential value that big data represents. Capitalizing on the true value of big data requires a completely different approach. Analytics Insight captures an exclusive interaction with Rion Graham, Data Scientist at GoodData, who highlights how harnessing effective big data analytics platforms can help organizations successfully drive real business impact.


Analytics Insight: Kindly brief us about the company, its specialization and the services that your company offers.

Rion: GoodData is an integrated set of data management, analytics, and insight application deployment and management tools and is a leader in the Platform as a Service category. GoodData combines an organization’s internal and/or external data (both structured and unstructured) to deliver business critical insights to users. GoodData goes beyond traditional business intelligence and analytics delivering insights at the moment of action to drive better business outcomes. The company primarily serves insurance, retail, financial services, and ISV customers, but works with various other industries as well.


Analytics Insight: Tell us how your company is contributing in the Big Data Analytics industry and how the company is benefiting the clients.

Rion: Companies of all sizes are suffering from data overload. As the scope and scale of their data increases, attempting to generate meaningful insights with traditional BI tools quickly leads to negative returns. GoodData was engineered from the ground-up to easily handle massive data sets and high concurrency, thereby giving all its users only the data they need, when and where they need it, to make a spectrum of business decisions from tactical to highly strategic.


Analytics Insight: Kindly share your point of view on the current scenario of the Big Data Analytics and its future.

Rion: The analytics product market remains focused on visualizations as a key differentiator. There’s good reason for this, as they help to turn a series of numbers into a story with real-world business context. However, visualizations alone are often insufficient to describe the sheer volume and variety of metrics that are distributed and unstructured big-data is generated. Artificial intelligence (AI) is ideally suited to bridge this gap and is already being used to automate the process of insight discovery. As the role of AI within analytics expands, a new dialect of insights will emerge to complement existing visualizations.


Analytics Insight: How are disruptive technologies like big data analytics/AI/Machine Learning impacting today’s innovation?

Rion: Machine learning (ML) and AI are impacting innovation in part by extending the domain of analytics beyond traditional tabular business data to include images, video, unstructured text, and more. One interesting example is the use of AI and natural language processing (NLP) to identify and remove malicious bots and other bad actors on social media platforms by identifying patterns of interaction and conversation. Rather than listing static lists of blacklisted words, new AI algorithms can look at new individual users and how their language evolves over time — and how that relates to the platform as a whole. Not only semantic tagging, but also audio/visual tagging.


Analytics Insight: How is your company helping customers deliver relevant business outcomes through adoption of the company’s technology innovations?

Rion: We work closely with our clients to understand their underlying critical business needs and thereby design with specific end-users in mind, delivering only the data they need, in a manner they expect. By integrating ML-driven insights at the point of work, whether they be classifications, predictions, or suggested actions, we close the insight-to-action loop and drive ROI. For example, we used ML to help one of our clients (a billion-dollar per year company) redesign its quarterly renewals forecasting process — replacing a series of manual, Excel-based business processes and turning them into a streamlined program for greater data accuracy and a broader spectrum of insights. It’s now obvious to users what actions they should take based on the data presented so they are able make a real impact on their business.


Analytics Insight: What is the edge your company has over other players in the industry?

Rion: GoodData has been built from the ground up to distribute analytics at the point of work. While competitors struggle to connect insights with their audience, especially at scale, GoodData can deploy analytics and ML models to multiple personas and many thousands of users with ease. Additionally, GoodData’s platform provides continuous retraining of ML models, which means they don’t live in a vacuum. Rather, models and their end-users work in coordination to define and improve real-world business practices.


Analytics Insight: How does your company’s strategy facilitate the transformation of an enterprise?

Rion: Currently, most companies don’t view analytics as “mission critical”. GoodData’s customers rely on our platform largely when the abundance of their data mandates a shift in analytics from being a “cost center” to a mission-critical activity. Our approach of distributing embedded analytics at the point of work highlights actionable insights to the end users, transforming transactional reports into strategic decisions.


Analytics Insight: Which industry verticals are you currently focusing on? And what is your go-to-market strategy for the same?

Rion: Our focus is the insurance and financial services industries, as well as ISVs (Independent Software Vendors). Data is front-and-center for both industries, with complex insights driving decisions from the field to the boardroom. Within the insurance industry, for example, there are a lot of costly processes that insurers have to go through in order to validate claims. ML can eliminate some of these processes using tools like image recognition that can automatically classify potential damage through a claimant-submitted photo. Through our ML offerings, we’re especially suited not only to streamline repetitive calculations but also optimize high risk decisions critical to a company’s bottom line.

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